Ecology

1.Yesson, C., Clark, M. R., Taylor, M. L. & Rogers, A. D. The global distribution of seamounts based on 30 arc seconds bathymetry data. Deep. Res. Part I Oceanogr. Res. Pap. 58, 442–453 (2011).ADS 
Article 

Google Scholar 
2.Preez, CDu., Curtis, J. M. R. & Clarke, M. E. The structure and distribution of benthic communities on a shallow seamount (Cobb Seamount, Northeast Pacific Ocean). PLoS ONE 11, 1–29 (2016).Article 
CAS 

Google Scholar 
3.Auster, P. J. et al. Definition and detection of vulnerable marine ecosystems on the high seas: problems with the ‘move-on’ rule. ICES J. Mar. Sci. 68, 254–264 (2011).Article 

Google Scholar 
4.Watling, L. & Auster, P. J. Seamounts on the high seas should be managed as vulnerable marine ecosystems. Front. Mar. Sci. 4, 1–4 (2017).Article 

Google Scholar 
5.Cho, W. W. Faunal Biogeography, Community Structure, and Genetic Connectivity of North Atlantic Seamounts (Massachusetts Institute of Technology & Woods Hole Oceanographic Institution, 2008).6.Rogers, A. D. The Biology of Seamounts: 25 Years on. Advances in Marine Biology vol. 79 (Elsevie, 2018).7.Wagner, D. et al. The Salas y Gómez and Nazca ridges: a global diversity hotspot in need of protection. 28 (2020).8.Kvile, K. O., Taranto, G. H., Pitcher, T. J. & Morato, T. A global assessment of seamount ecosystems knowledge using an ecosystem evaluation framework. Biol. Conserv. 173, 108–120 (2014).Article 

Google Scholar 
9.Victorero, L., Robert, K., Robinson, L. F., Taylor, M. L. & Huvenne, V. A. I. Species replacement dominates megabenthos beta diversity in a remote seamount setting. Sci. Rep. 8, 1–11 (2018).CAS 
Article 

Google Scholar 
10.Yesson, C. et al. Improved bathymetry leads to 4000 new seamount predictions in the global ocean. UCL Open Environ. Preprint, 1–12 (2020).11.Gálvez Larach, M. Montes submarinos de Nazca y Salas y Gómez: una revisión para el manejo y conservación. Lat. Am. J. Aquat. Res. 37, 479–500 (2009).Article 

Google Scholar 
12.Jarrard, R. D. & Clague, D. A. Implications of Pacific Island and seamount ages for the origin of volcanic chains. Rev. Geophys. 15, 57–76 (1977).ADS 
Article 

Google Scholar 
13.Chave, E. H. & Jones, A. T. Deep-water megafauna of the Kohala and Haleakala slopes, Alenuihaha Channel Hawaii. Deep Sea Res. Part A Oceanogr. Res. Pap. 38, 781–803 (1991).ADS 
Article 

Google Scholar 
14.Kitchingman, A., Lai, S., Morato, T. & Pauly, D. How many seamounts are there and where are they located? In Seamounts: Ecology, Fisheries & Conservation, Series 12 (eds Pitcher, T. J. et al.) 26–40 (Blackwell Publishing, 2008). https://doi.org/10.1002/9780470691953.ch2.
Google Scholar 
15.Parin, N. V., Mironov, A. N. & Nesis, K. M. Biology of the Nazca and Sala y Gómez submarine ridges, an outpost of the Indo-West Pacific fauna in the eastern Pacific ocean: composition and distribution of the fauna, its communities and history. Advances in Marine Biology vol. 32 (1997).16.Samadi, S., Schlacher, T. & Richer de Forges, B. Seamount benthos. In Seamounts: Ecology, Fisheries and Conservation (eds Pitcher, T. et al.) 119–140 (Wiley-Blackwell, 2007).
Google Scholar 
17.Mironov, A. N., Molodtsova, T. N. & Parin., N. V. Soviet and Russian studies on seamount biology. (2006).18.Fernández, M., Pappalardo, P., Rodríguez-Ruiz, M. C. & Castilla, J. C. Síntesis del estado del conocimiento sobre la riqueza de especies de macroalgas, macroinvertebrados y peces en aguas costeras y oceánicas de Isla de Pascua e Isla Salas y Gómez. Lat. Am. J. Aquat. Res. 42, 760–802 (2014).Article 

Google Scholar 
19.Easton, E. E. et al. Chile and the Salas y Gómez Ridge. In Mesophotic Coral Ecosystems 477–490 (Springer, 2019). https://doi.org/10.1007/978-3-319-92735-0_27.20.Friedlander, A. M. et al. Marine biodiversity in Juan Fernández and Desventuradas islands, Chile: global endemism hotspots. PLoS ONE 11, e0145059 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Sellanes, J., Salisbury, R. A., Tapia, J. M. & Asorey, C. M. A new species of Atrimitra Dall, 1918 (Gastropoda: Mitridae) from seamounts of the recently created Nazca-Desventuradas Marine Park Chile. PeerJ 2019, 1–16 (2019).
Google Scholar 
22.Gaymer, C. F. et al. Plan General de Administración y su Valoración Económica. Informe final proyecto FIPA 2016–31 ‘Bases técnicas para la gestión del Parque Marino Nazca-Desventuradas y propuesta de Plan General de Administración’ (2018).23.Clark, M. R. et al. The ecology of seamounts: structure, function, and human impacts. Ann. Rev. Mar. Sci. 2, 253–278 (2010).PubMed 
Article 

Google Scholar 
24.Henry, L. A. et al. Environmental variability and biodiversity of megabenthos on the Hebrides Terrace Seamount (Northeast Atlantic). Sci. Rep. 4, 1–10 (2014).
Google Scholar 
25.Jones, C. G., Lawton, J. H. & Shachak, M. Organisms as ecosystem engineers. Oikos 69, 373 (1994).Article 

Google Scholar 
26.Morgan, N. B., Goode, S., Roark, E. B. & Baco, A. R. Fine scale assemblage structure of benthic invertebrate megafauna on the North Pacific Seamount Mokumanamana. Front. Mar. Sci. 6, 1–21 (2019).Article 

Google Scholar 
27.Davies, J. S. et al. Benthic assemblages of the Anton Dohrn Seamount (NE Atlantic): defining deep-sea biotopes to support habitat mapping and management efforts with a focus on vulnerable marine ecosystems. PLoS ONE 10, 33 (2015).
Google Scholar 
28.Auster, P. J., Malatesta, R. J. & Larosa, S. C. Patterns of microhabitat utilization by mobile megafauna on the southern New England (USA) continental shelf and slope. Mar. Ecol. Prog. Ser. 127, 77–85 (1995).ADS 
Article 

Google Scholar 
29.Uzmann, J. R., Cooper, R. A., Theroux, R. B. & Wigley, R. L. Synoptic comparison of three sampling techniques for estimating abundance and distribution of selected megafauna: submersible vs. camera sled vs. otter trawl. Mar. Fish. Rev. 39, 11–19 (1977).
Google Scholar 
30.Valentine, J. P. & Edgar, G. J. Impacts of a population outbreak of the urchin Tripneustes gratilla amongst Lord Howe Island coral communities. Coral Reefs 29, 399–410 (2010).ADS 
Article 

Google Scholar 
31.Greene, H. et al. A classification scheme for deep seafloor habitats. Oceanol. Acta 22, 663–678 (1999).Article 

Google Scholar 
32.Greene, H., O’Connell, V., Brylinsky, C. & Reynolds, J. Marine Benthic Habitat classification: What’s Best for Alaska? In Marine Habitat Mapping Technology for Alaska (eds Reynolds, J. & Greene, H. G.) 169–184 (Alaska Sea Grant College Program University of Alaska Fairbanks, 2008). https://doi.org/10.4027/mhmta.2008.12.
Google Scholar 
33.Naar, D. F., Johnson, K. P., Wessel, D., Duncan, P. & Mahoney, J. Rapa Nui. 2001: Cruise report for Leg 6 of the Drift expedition aboard the R/V Revelle (2001).34.Haase, K. M., Stoffers, P. & Garbe-Schönberg, C. D. The petrogenetic evolution of lavas from Easter Island and neighbouring seamounts, near-ridge hotspot volcanoes in the SE pacific. J. Petrol. 38, 785–813 (1997).ADS 
CAS 
Article 

Google Scholar 
35.Woods, M. T. & Okal, E. A. The structure of the Nazca Ridge and Sala y Gomez seamount chain from the dispersion of Rayleigh waves. Geophys. J. Int. 117, 205–222 (1994).ADS 
Article 

Google Scholar 
36.Rodrigo, C., Foucher, N., Philippi, N. & Lara, L. E. Morfoestructuras volcánicas y sedimentarias de los montes submarinos de la región de las islas Desventuradas, basadas en el análisis de datos acústicos. 110–115 (2017).37.Mecho, A. et al. Environmental drivers of mesophotic echinoderm assemblages of the Southeastern Pacific Ocean. Front Mar. Sci. 8, 1–15 (2021).Article 

Google Scholar 
38.VLC media player – Open Source Multimedia Framework and Player.39.Dyer, B. S. & Westneat, M. W. Taxonomía y biogeografía de los peces costeros del Archipiélago de Juan Fernández y de las islas Desventuradas Chile. Rev. Biol. Mar. Oceanogr. 45, 589–617 (2010).Article 

Google Scholar 
40.Pequeño, G. & Lamilla, J. The Littoral Fish Assemblage of the Desventuradas Islands (Chile) Has Zoogeographical Affinities with the Western Pacific. Glob. Ecol. Biogeogr. 9, 431–437 (2000).Article 

Google Scholar 
41.Raines, B. & Huber, M. Biodiversity Quadrupled-Revision of Easter Island and Salas y Gómez Bivalves. Zootaxa 106 (2012).42.Retamal, M. A. & Moyano, H. I. Zoogeografía de los crustáceos decápodos chilenos marinos y dulceacuícolas. Lat. Am. J. Aquat. Res. 38, 302–328 (2010).
Google Scholar 
43.Sysoev, A. B. Gastropods of the family Turridae (Gastropoda:Toxoglosa) of the Nasca and Sala y Gómez underwater ridges. 124, 245–260 (1990).44.Zarenkov, N. A. Crabs of the familiy Leucosiidae (subfamilies Ebalinae an Iliinae) collected in tropical water of Indian and Pacific oceans waters of Indian and Pacific oceans. Bol. Nauk. 10, 16–26 (1969).
Google Scholar 
45.Zarenkov, N. A. Decapods (Stenopodidea, Brachyura, Anomura) of the underwater Nazca and Salas y Gómez Ridges. Tr. Instituta Okeanol. AN USSR 124, 218–244 (1990).
Google Scholar 
46.Barriga, E., Salazar, C., Palacios, J., Romero, M. & Rodriguez, A. Distribucion, abundancia y estructura poblacional del langostino rojo de profundidad Haliporoides diomedeae (Crustacea: Decapoda: Solenoceridae). Lat. Am. J. Aquat. Res. 37, 371–380 (2009).
Google Scholar 
47.R Core Team. R Core Team (2020). R: A language and environment for statistical computing. version 4.0.3. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019).48.Oksanen J et al. vegan: Community Ecology Package.R package version 2.5-7. https://cran.r-project.org/package=vegan (2020).49.Jones, D. & Frid, C. L. J. Altering intertidal sediment topography: effects on biodiversity and ecosystem functioning. Mar. Ecol. 30, 83–96 (2009).ADS 
Article 

Google Scholar 
50.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).
Google Scholar 
51.National Geographic & Oceana. Islas Desventuradas. Biodiversidad marina y propuesta de conservación. 58 (2013).52.Levin, L. A. & Nittrouer, C. A. Textural characteristics of sediment on deep seamounts in the eastern Pacific Ocean between 10°N and 30°N. In Seamounts, Islands and Atolls, 43 (eds Keating, B. et al.) 187–203 (Geophysical Monograph, 1987).
Google Scholar 
53.Lourido, A., Parra, S. & Serrano, A. Preliminary Results on the Composition and Structure of Soft-Bottom Macrobenthic Communities of a Seamount: the Galicia Bank (NE Atlantic Ocean). Thalassas 35, 1–9 (2019).Article 

Google Scholar 
54.Flach, E., Muthumbi, A. & Heip, C. Meiofauna and macrofauna community structure in relation to sediment composition at the iberian margin compared to the goban spur (NE atlantic). Prog. Oceanogr. 52, 433–457 (2002).ADS 
Article 

Google Scholar 
55.Levin, L. A. & Gooday, A. The deep Atlantic Ocean floor. In Ecosystems of the Deep Oceans (ed. Tyler, P.) 187–203 (Elsevier, 2003).
Google Scholar 
56.Thistle, D. The deep-sea floor: an overview. In Ecosystems of the World, Ecosystems of the Deep Sea (ed. Tyler, P. A.) 5–37 (Elsevier, 2003).
Google Scholar 
57.Louzao, M. et al. Historical macrobenthic community assemblages in the Avilés Canyon, N Iberian Shelf: Baseline biodiversity information for a marine protected area. J. Mar. Syst. 80, 47–56 (2010).Article 

Google Scholar 
58.Kon, K., Tsuchiya, Y., Sato, T., Shinagawa, H. & Yamada, Y. Role of microhabitat heterogeneity in benthic faunal communities in sandy bottom sediments of Oura Bay, Shimoda Japan. Reg. Stud. Mar. Sci. 2, 71–76 (2015).Article 

Google Scholar 
59.Clark, M. R., Schlacher, T. A., Rowden, A. A., Stocks, K. I. & Consalvey, M. Science priorities for Seamounts: research links to conservation and management. PLoS ONE 7, e29232 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Zeppilli, D., Pusceddu, A., Trincardi, F. & Danovaro, R. Seafloor heterogeneity influences the biodiversity-ecosystem functioning relationships in the deep sea. Sci. Rep. 6, 1–12 (2016).Article 
CAS 

Google Scholar 
61.de la Torriente, A. et al. Benthic habitat modelling and mapping as a conservation tool for marine protected areas: a seamount in the western Mediterranean. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 732–750 (2019).Article 

Google Scholar 
62.Gallardo, M., Macpherson, E., Tapia-Guerra, J. M., Asorey, C. M. & Sellanes, J. A new species of Munida Leach, 1820 (Crustacea: Decapoda: Anomura: Munididae) from seamounts of the Nazca-Desventuradas Marine Park. PeerJ https://doi.org/10.7717/peerj.10531 (2021).Article 

Google Scholar 
63.Castilla, J. C. Islas oceánicas chilenas: conocimiento científico y necesidades de investigación (Ediciones Universidad Católica de Chile, 1987).64.Bahamonde, N. San Félix y San Ambrosio, las islas llamadas Desventuradas 85–99 (1987).65.Díaz-Díaz, O., Bone, D., Rodríguez, C. T. & Delgado-Blas, V. H. Poliquetos de Sudamérica. Especial d, 149 (2017).66.Díaz-Díaz, O. F., Rozbaczylo, N., Sellanes, J. & Tapia-Guerra, J. M. A new species of Eunice Cuvier, 1817 (Polychaeta: Eunicidae) from the slope of the Desventuradas Islands and seamounts of the Nazca Ridge, southeastern Pacific Ocean. A New Species Cuscus 4860, 211–226 (2020).
Google Scholar 
67.Kantor, Y. & Sysoev, A. Latiaxis (Babelomurex) naskensis, a new species of Coralliophilidae (Gastropoda) from South-Eastern Pacific. Ruthenica 2, 163–167 (1992).
Google Scholar 
68.Sepulveda, J. I. Peces de las Islas Oceánicas Chilenas. In Islas Oceánicas Chilenas: Conocimiento científico y necesidades de Investigaciones. (ed. Castilla, J.) 225–246 (Ediciones Universidad Católica de Chile, 1987).69.Mironov, A. & Detinova., N. Bottom fauna of the Nazca and Sala y Gomez ridges. Plankton and benthos from the Nazca and Sala y Gomez Submarine Ridges 269–278 (1990).70.Lundsten, L. et al. Benthic invertebrate communities on three seamounts off southern and central California USA. Mar. Ecol. Prog. Ser. 374, 23–32 (2009).ADS 
Article 

Google Scholar 
71.Rex, M. A. et al. Global bathymetric patterns of standing stock and body size in the deep-sea benthos. Mar. Ecol. Prog. Ser. 317, 1–8 (2006).ADS 
Article 

Google Scholar 
72.QGIS.org. QGIS Geographic Information System.QGIS Association. Version 3.10. https://www.qgis.org (2020). More

1.Sharrow, S. H. & Ismail, S. Carbon and nitrogen storage in agroforests, tree plantations, and pastures in western Oregon, USA. Agrofor. Syst. 60(2), 123–130 (2004).Article 

Google Scholar 
2.Yang, L. L. et al. Carbon and nitrogen storage and distribution in four forest ecosystems in Liupan Mountains, Northwestern China. Acta. Ecol. Sin. 35(15), 5215–5227 (2015).
Google Scholar 
3.Watson, R. T. et al. Land use, land-use change, and forestry. In: Published for the Intergovernmental Panel on Climate Change. Cambridge University Press, pp. 308 (2000).4.Zhao, M. M. et al. Estimation of China’s forest stand biomass carbon sequestration based on the continuous biomass expansion factor model and seven forest inventories from 1977 to 2013. For. Ecol. Manag. 448, 528–534 (2019).Article 

Google Scholar 
5.Dale, V. H. et al. Climate change and forest disturbances. Bioscience 51, 723–734 (2001).Article 

Google Scholar 
6.Gunderson, P. Carbon—Nitrogen Interactions in Forest Ecosystems—Final Report. Danish Centre for Forest, Landscape and Planning, Denmark (2006).7.Hook, P. B. & Burke, I. C. Biogeochemistry in a shortgrass landscape: control by topography, soil texture, and microclimate. Ecology 81, 2686–2703 (2000).Article 

Google Scholar 
8.Vourlitis, G. L., Zorba, G., Pasquini, S. C. & Mustard, R. Carbon and nitrogen storage in soil and litter of southern Californian semi-arid shrublands. J. Arid Environ. 70, 164–173 (2007).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Liu, G. H., Fu, B. & Fang, J. Y. Carbon dynamics of Chinese forests and its contribution to global carbon balance. Acta. Ecol. Sin. 20(5), 733–740 (2000).
Google Scholar 
11.IPCC. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge (2007).12.Phillips, J. et al. Live aboveground carbon stocks in natural forests of Colombia. For. Ecol. Manag. 374, 119–128 (2016).Article 

Google Scholar 
13.Gibbs, H. K., Brown, B., Niles, J. O. & Foley, J. A. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ. Res. Lett. 2(4), 1–13 (2007).
Google Scholar 
14.Aragão, L. et al. Above- and below-ground net primary productivity across ten Amazonian forests on contrasting soils. Biogeosciences 6, 2759–2778 (2009).ADS 
Article 

Google Scholar 
15.Malhi, Y. et al. Comprehensive assessment of carbon productivity, allocation and storage in three Amazonian forests. Glob. Chang. Biol. 15, 1255–1274 (2009).ADS 
Article 

Google Scholar 
16.Post, W. M. & Kwon, K. C. Soil carbon sequestration and land use change: processes and potential. Glob. Chang. Biol. 6, 317–327 (2000).ADS 
Article 

Google Scholar 
17.Ma, J. et al. Ecosystem carbon storage distribution between plant and soil in different forest types in Northeastern China. Ecol. Eng. 81, 353–362 (2015).Article 

Google Scholar 
18.Davidson, E. A., Trumbore, S. E. & Amundson, R. Biogeochemistry—soil warming and organic carbon content. Nature 408, 789–790 (2000).ADS 
CAS 
PubMed 
Article 

Google Scholar 
19.Chaturvedi, R. K. & Raghubanshi, A. S. Aboveground biomass estimation of small diameter woody species of tropical dry forest. New For. 44, 509–519 (2013).Article 

Google Scholar 
20.Wen, D. & He, N. P. Forest carbon storage along the north-south transect of eastern china: spatial patterns, allocation, and influencing factors. Ecol. Indic. 61, 960–967 (2016).CAS 
Article 

Google Scholar 
21.Fan, S. et al. A large terrestrial carbon sink in North America implied by atmospheric andoceanic carbon dioxide data and models. Science 282, 442–446 (1998).ADS 
CAS 
PubMed 
Article 

Google Scholar 
22.Gough, C. M., Vogel, C. S., Schmid, H. P. & Curtis, P. S. Controls on annual forest carbon storage: lessons from the past and predictions for the future. Bioscience 58, 609–622 (2008).Article 

Google Scholar 
23.Van Deusen, P. Carbon sequestration potential of forest land: Management for products and bioenergy versus preservation. Biomass Bioenerg. 34, 1687–1694 (2010).Article 

Google Scholar 
24.Bradford, J. B., Jensen, N. R., Domke, G. M. & D’Amato, A. W. Potential increases in natural disturbance rates could offset forest management impacts on ecosystem carbon stocks. For. Ecol. Manag. 308, 178–187 (2013).Article 

Google Scholar 
25.Park, A. Carbon storage and stand conversion in a pine-dominated boreal forest landscape. For. Ecol. Manag. 340, 70–81 (2015).Article 

Google Scholar 
26.Wang, S. J., Zhao, J. X. & Chen, Q. B. Controlling factors of soil CO2 efflux in Pinusyunnanensis across different stand ages. PLoS ONE 10(5), e0127274. https://doi.org/10.1371/journal.pone.0127274 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
27.Liu, J. et al. Distinct soil bacterial communities in response to the cropping system in a Mollisol of northeast China. Appl. Soil Ecol. 119, 407–416 (2017).Article 

Google Scholar 
28.Kavvadias, V. A. et al. Litterfall, litter accumulation and litter decomposition rates in four forest ecosystems in northern Greece. For. Ecol. Manag. 144, 113–127 (2001).Article 

Google Scholar 
29.Dai, W. et al. Spatial pattern of carbon stocks in forest ecosystems of a typical subtropical region of Southeastern China. For. Ecol. Manag. 409, 288–297 (2018).Article 

Google Scholar 
30.Liu, S. et al. Carbon and nitrogen storage and distribution in different forest ecosystems in the subalpine of western Sichuan. Acta. Ecol. Sin. 37(4), 1074–1083 (2017).CAS 
Article 

Google Scholar 
31.Kern, J., Giani, L., Teixeira, W., Lanza, G. & Glaser, B. What can we learn from ancient fertile anthropic soil (Amazonian Dark Earths, shell mounds, Plaggen soil) for soil carbon sequestration?. CATENA 172, 104–112 (2019).CAS 
Article 

Google Scholar 
32.Zhang, Z. H., Wang, L. C., Luo, J. X. & Zheng, D. R. Study on tree biomass models of Pinus Yunnanensis Faranch in Northwest Yunnan Province. J. Shandong For. Sci. Technol. 4, 4–6 (2011) ((in Chinese)).ADS 

Google Scholar 
33.Chen, C. Biomass and production of the Arbor-Layers in Pinus armandii forests. J. Northwestern Coll. For. 1, 1–18 (1984) ((in Chinese)).
Google Scholar 
34.Liu, S. R., Su, Y. M., Cai, X. H. & Ma, Q. Y. Aboveground biomass of quercus aquifolioides shrub community and its responses to altitudinal gradients in balangshan mountain, Shichuan province. Sci. Silvae. Sin. 42, 1–7 (2006) ((in Chinese)).
Google Scholar 
35.Li, J. L., Liang, S. C. & Chen, S. Z. A preliminary study on the biomass models of keteleeria davidiana var chien-peii colony in qingyan town of Guizhou province. J. Guizhou Normal Univ. 15, 7–12 (1997) ((in Chinese)).CAS 

Google Scholar 
36.Yang, L. L. et al. Carbon and nitrogen storage and distribution in four forest ecosystems in Liupan Mountains, northwestern China. Acta. Ecol. Sin. 35, 5215–5227 (2015) ((in Chinese)).
Google Scholar 
37.Xie, S. C., Liu, W. Y., Li, S. C. & Yang, G. P. Preliminary studies on the biomass of middle-mountain moist evergreen broadleaved forests in Ailao Mountain, Yunnan. Acta Phytoecol. Sin. 20, 167–176 (1996) ((in Chinese)).
Google Scholar 
38.Shen, Y., Tian, D. L., Yan, W. D. & Xiao, Y. Biomass and its distribution of natural secondary quercus fabri + sassafras tsumu+ cunninghamia lanceolata community in Yuanling county, Hunan province. J. Cent. South Univ. For. Technol. 31, 44–51 (2011) ((in Chinese)).CAS 

Google Scholar 
39.Guo, L. B. & Gifford, R. M. Soil carbon stocks and land use change: a meta analysis. Global Change Biol. 8, 345–360 (2002).ADS 
Article 

Google Scholar 
40.Zhou, Y. R., Yu, Z. L. & Zhao, S. D. Carbon storage and budget of major Chinese forest types. Acta. Phytoecol. Sin. 24, 518–522 (2000) ((in Chinese)).
Google Scholar 
41.Eslamdoust, J. & Sohrabi, H. Carbon storage in biomass, litter, and soil of different native and introduced fast-growing tree plantations in the South Caspian Sea. J. For. Res. 29, 449–457 (2018).CAS 
Article 

Google Scholar 
42.He, Y. J. et al. Carbon storage capacity of monoculture and mixed-species plantations in subtropical China. For. Ecol. Manag. 295, 193–198 (2013).Article 

Google Scholar 
43.Ren, H. et al. Spatial and temporal patterns of carbon storage from 1992 to 2002 in forest ecosystems in Guangdong, Southern China. Plant Soil 363, 123–138 (2013).CAS 
Article 

Google Scholar 
44.Ali, F., Khan, N., Ahmad, A. & Khan, A. A. Structure and biomass carbon of Olea ferruginea forests in the foot hills of Malakand division, Hindukush range mountains of Pakistan. Acta. Ecol. Sin. 39, 261–266 (2019).Article 

Google Scholar 
45.Ren, Y. et al. Potential for forest vegetation carbon storage in Fujian Province, China, determined from forest inventories. Plant Soil 345, 125–140 (2011).CAS 
Article 

Google Scholar 
46.Fu, W. J. et al. Spatial variation of biomass carbon density in a subtropical region of Southeastern China. Forests 6, 1966–1981 (2015).Article 

Google Scholar 
47.Fonseca, W., Alice, F. E. & Rey-Benayas, J. M. Carbon accumulation in aboveground and belowground biomass and soil of different age native forest plantations in the humid tropical lowlands of Costa Rica. New For. 43, 197–211 (2012).Article 

Google Scholar 
48.Nelson, A., Saunders, M., Wagner, R. & Weiskittel, A. Early stand production of hybrid poplar and white spruce in mixed and monospecific plantations in eastern Maine. New For. 43, 519–534 (2012).Article 

Google Scholar 
49.Gao, Y., Cheng, J., Ma, Z., Zhao, Y. & Su, J. Carbon storage in biomass, litter, and soil of different plantations in a semiarid temperate region of northwest China. Ann. For. Sci. 71, 427–435 (2014).Article 

Google Scholar 
50.Fortier, J., Gagnon, D., Truax, B. & Lambert, F. Biomass and volume yield after 6 years in multiclonal hybrid poplar riparian buffer strips. Biomass Bioenerg. 34, 1028–1040 (2010).Article 

Google Scholar 
51.González-Rodríguez, H. et al. Litterfall deposition and leaf litter nutrient return in different locations at Northeastern Mexico. Plant Ecol. 212, 1747–1757 (2011).Article 

Google Scholar 
52.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science https://doi.org/10.1126/science.1201609 (2011).Article 
PubMed 

Google Scholar 
53.Bradford, J. B., Birdsey, R. A., Joyce, L. A. & Ryan, M. G. Tree age, disturbance history and carbon stocks and fluxes in subalpine rocky mountain forests. Global Change Biol. 14, 2882–2897 (2008).ADS 
Article 

Google Scholar 
54.Zhang, C. N., Yan, X. D. & Yang, J. H. Estimation of nitrogen reserves in forest soils of China. J. Southwest Agric. Univ. 26, 572-575+579 (2004) ((in Chinese)).
Google Scholar 
55.Lee, K. L., Ong, K. H., King, P. J. H., Chubo, J. K. & Su, D. S. A. Stand productivity, carbon content, and soil nutrients in different stand ages of Acacia mangium in Sarawak, Malaysia. Turk. J. Agric. For. 39, 154–161 (2015).CAS 
Article 

Google Scholar 
56.Cao, B., Domke, G. M., Russell, M. B. & Walters, B. F. Spatial modeling of litter and soil carbon stocks on forest land in the conterminous United States. Sci. Total Environ. 654, 94–106 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
57.Deng, L., Wang, K. B., Chen, M. L., Shangguan, Z. P. & Sweeney, S. Soil organic carbon storage capacity positively related to forest succession on the Loess Plateau, China. CATENA 110, 1–7 (2013).CAS 
Article 

Google Scholar 
58.Zhu, B. et al. Altitudinal changes in carbon storage of temperate forests on Mt Changbai, Northeast China. J. Plant Res. 123, 439–452 (2010).PubMed 
Article 

Google Scholar 
59.Xie, X. L., Sun, B., Zhou, H. Z. & Li, A. B. Soil organic carbon storage in China. Pedosphere 14, 491–500 (2004).CAS 

Google Scholar 
60.Leuschner, C., Moser, G., Bertsch, C., Röderstein, M. & Hertel, D. Large altitudinal increase in tree root/shoot ratio in tropical mountain forests of Ecuador. Basic Appl. Ecol. 8, 219–230 (2007).Article 

Google Scholar 
61.Singh, S. P., Adhikari, B. S. & Zobel, D. B. Biomass, productivity, leaf longevity, and forest structure in the central Himalaya. Ecol. Monog. 64, 401–421 (1994).Article 

Google Scholar 
62.Kirschbaum, M. U. F. Will changes in soil organic carbon act as a positive or negative feedback on global warming?. Biogeochemistry 27, 753–760 (2000).Article 

Google Scholar 
63.Raich, J. W., Russel, A. E., Kitayama, K., Parton, W. J. & Vitousek, P. M. Temperature influences carbon accumulation in moist tropical forests. Ecology 87, 76–87 (2006).PubMed 
Article 

Google Scholar  More

1.Endler, J. A. Signals, signal conditions, and the direction of evolution. Am. Nat. 139, S125–S153 (1992).Article 

Google Scholar 
2.Endler, J. A. Some general comments on the evolution and design of animal communication systems. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 340, 215–225 (1993).ADS 
CAS 
PubMed 
Article 

Google Scholar 
3.Fleishman, L. J. The influence of the sensory system and the environment on motion patterns in the visual displays of anoline lizards and other vertebrates. Am. Nat. 139, S36–S61 (1992).Article 

Google Scholar 
4.Lythgoe, J. N. The Ecology of vision (Oxford University Press, 1979).
Google Scholar 
5.Bradbury, J. W. & Vehrencamp, S. L. Principles of Animal Communication 2nd edn. (Sinauer Associates, 1998).
Google Scholar 
6.Morton, E. S. Ecological sources of selection on avian sounds. Am. Nat. 109, 17–34 (1975).ADS 
Article 

Google Scholar 
7.Endler, J. A. On the measurement and classification of colour in studies of animal colour patterns. Biol. J. Linn. Soc. Lond. 41, 315–352 (1990).Article 

Google Scholar 
8.Wiley, R. H. & Richards, D. G. Adaptations for acoustic communication in birds: Sound transmission and signal detection. In Ecology and Evolution of Acoustic Communication in Birds (eds Kroodsma, D. E. & Miller, E. H.) 131–181 (Academic Press, 1983).
Google Scholar 
9.Bernard, G. D. & Remington, C. L. Color vision in Lycaena butterflies: Spectral tuning of receptor arrays in relation to behavioral ecology. Proc. Natl. Acad. Sci. USA 88, 2783–2787 (1991).ADS 
CAS 
PubMed 
Article 

Google Scholar 
10.Peters, R. A., Clifford, C. W. G. & Evans, C. S. Measuring the structure of dynamic visual signals. Anim. Behav. 64, 131–146 (2002).Article 

Google Scholar 
11.Narins, P. M. Seismic communication in anuran amphibians. Bioscience 40, 268–274 (1990).Article 

Google Scholar 
12.Fleishman, L. & Persons, M. The influence of stimulus and background colour on signal visibility in the lizard Anolis cristatellus. J. Exp. Biol. 204, 1559–1575 (2001).CAS 
PubMed 

Google Scholar 
13.Brumm, H. & Slabbekoorn, H. Acoustic communication in noise. Adv. Study Behav. 35, 151–209 (2005).Article 

Google Scholar 
14.Peters, R. A., Hemmi, J. M. & Zeil, J. Signaling against the wind: modifying motion-signal structure in response to increased noise. Curr. Biol. 17, 1231–1234 (2007).CAS 
PubMed 
Article 

Google Scholar 
15.Ord, T. J. & Stamps, J. A. Alert signals enhance animal communication in “noisy” environments. Proc. Natl. Acad. Sci. USA 105, 18830–18835 (2008).ADS 
CAS 
PubMed 
Article 

Google Scholar 
16.Komers, P. E. Behavioural plasticity in variable environments. Can. J. Zool. 75, 161–169 (1997).Article 

Google Scholar 
17.Ord, T. J., Charles, G. K., Palmer, M. & Stamps, J. A. Plasticity in social communication and its implications for the colonization of novel habitats. Behav. Ecol. 27b, 341–351 (2015).
Google Scholar 
18.Marten, K. & Marler, P. Sound transmission and its significance for animal vocalization. Behav. Ecol. Sociobiol. 2, 271–290 (1977).Article 

Google Scholar 
19.Ryan, M. J., Cocroft, R. B. & Wilczynski, W. The role of environmental selection in intraspecific divergence of mate recognition signals in the cricket frog, Acris crepitans. Evolution 44, 1869–1872 (1990).PubMed 
Article 

Google Scholar 
20.Leal, M. & Fleishman, L. J. Differences in visual signal design and detectability between allopatric populations of Anolis lizards. Am. Nat. 163, 26–39 (2004).PubMed 
Article 

Google Scholar 
21.McNett, G. D. & Cocroft, R. B. Host shifts favor vibrational signal divergence in Enchenopa binotata treehoppers. Behav. Ecol. 19, 650–656 (2008).Article 

Google Scholar 
22.Ferguson, G. W. Variation and evolution of the push-up displays of the side-blotched lizard genus Uta (Iguanidae). Syst. Zool. 20, 79–101 (1971).Article 

Google Scholar 
23.Martins, E. P., Bissell, A. N. & Morgan, K. K. Population differences in a lizard communicative display: evidence for rapid change in structure and function. Anim. Behav. 56, 1113–1119 (1998).CAS 
PubMed 
Article 

Google Scholar 
24.Martins, E. P. & Lamont, J. Estimating ancestral states of a communicative display: A comparative study of Cyclurarock iguanas. Anim. Behav. 55, 1685–1706 (1998).CAS 
PubMed 
Article 

Google Scholar 
25.Bloch, N. & Irschick, D. An analysis of inter-population divergence in visual display behavior of the green anole lizard (Anolis carolinensis). Ethology 112, 370–378 (2006).Article 

Google Scholar 
26.Barquero, M. D., Peters, R. & Whiting, M. Geographic variation in aggressive signalling behaviour of the Jacky dragon. Behav. Ecol. Sociobiol. 69, 1501–1510 (2015).Article 

Google Scholar 
27.Bian, X., Chandler, T., Laird, W., Pinilla, A. & Peters, R. Integrating evolutionary biology with digital arts to quantify ecological constraints on vision-based behaviour. Methods Ecol. Evol. 9, 544–559 (2018).Article 

Google Scholar 
28.Fleishman, L. J. Motion detection in the presence and absence of background motion in an Anolis lizard. J. Comp. Physiol. A 159, 711–720 (1986).CAS 
PubMed 
Article 

Google Scholar 
29.Fleishman, L. J. Sensory and environmental influences on display form in Anolis auratus, a grass anole from Panama. Behav. Ecol. Sociobiol. 22, 309–316 (1988).
Google Scholar 
30.Eckert, M. P. & Zeil, J. Towards an ecology of motion vision. In Motion Vision (eds Zanker, J. M. & Zeil, J.) 333–369 (Springer, 2001).
Google Scholar 
31.Peters, R. A. & Evans, C. S. Design of the Jacky dragon visual display: Signal and noise characteristics in a complex moving environment. J. Comp. Physiol. A 189, 447–459 (2003).CAS 
Article 

Google Scholar 
32.Peters, R. A. Noise in visual communication: Motion from wind-blown plants. In Animal Communication and Noise. Animal Signals and Communication (ed. Brumm, H.) 311–330 (Springer, 2013).
Google Scholar 
33.Ramos, J. A. & Peters, R. A. Motion-based signaling in sympatric species of Australian agamid lizards. J. Comp. Physiol. A 203, 661–671 (2017).CAS 
Article 

Google Scholar 
34.Ramos, J. A. & Peters, R. A. Habitat-dependent variation in motion signal structure between allopatric populations of lizards. Anim. Behav. 126, 69–78 (2017).Article 

Google Scholar 
35.Ramos, J. A. & Peters, R. A. Quantifying ecological constraints on motion signaling. Front. Ecol. Evol. 5, 9 (2017).Article 

Google Scholar 
36.Bian, X., Chandler, T., Pinilla, A. & Peters, R. Now you see me, now you don’t: Environmental conditions, signaler behavior, and receiver response thresholds interact to determine the efficacy of a movement-based animal signal. Front. Ecol. Evol. 7, 130 (2019).Article 

Google Scholar 
37.Posner, M. I., Snyder, C. R. & Davidson, B. J. Attention and the detection of signals. J. Exp. Psychol. 109, 160–174 (1980).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Zeil, J. & Zanker, J. M. A glimpse into crabworld. Vis. Res. 37, 3417–3426 (1997).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Koch, C. & Ullman, S. Shifts in selective visual attention: Towards the underlying neural circuitry. in Matters of Intelligence. Conceptual Structures in Cognitie Neuroscience (ed. Vaina, L. M.) 115–142 (Springer, 1987).40.Itti, L., Koch, C. & Niebur, E. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20, 1254–1259 (1998).Article 

Google Scholar 
41.Harel, J., Koch, C. & Perona, P. Graph-based visual saliency. Adv. Neural Inf. Proc. Sys. 19, 545–552 (2006).
Google Scholar 
42.Koch, C. Biophysics of Computation: Information Processing in Single Neurons (Oxford University Press, 1998).
Google Scholar 
43.Tatler, B. W., Hayhoe, M. M., Land, M. F. & Ballard, D. H. Eye guidance in natural vision: Reinterpreting salience. J. Vis. 11, 5 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Wilson, S. & Swan, G. A Complete Guide to Reptiles of Australia 2nd edn. (Reed New Holland, 2013).
Google Scholar 
45.Heatwole, H. & Firth, B. T. Voluntary maximum temperature of the jacky lizard, Amphibolurus muricatus. Copeia 1982, 824–829 (1982).Article 

Google Scholar 
46.Harlow, P. S. & Taylor, J. E. Reproductive ecology of the jacky dragon (Amphibolurus muricatus): An agamid lizard with temperature-dependent sex determination. Aust. Ecol. 25, 640–652 (2000).Article 

Google Scholar 
47.Ord, T. J. & Evans, C. S. Display rate and opponent assessment in the Jacky dragon (Amphibolurus muricatus): An experimental analysis. Behaviour 140, 1495–1508 (2003).Article 

Google Scholar 
48.Warner, D. A. & Shine, R. Interactions among thermal parameters determine offspring sex under temperature-dependent sex determination. Proc. R. Soc. Lond. B. Biol. Sci. 278, 256–265 (2010).
Google Scholar 
49.Carpenter, C. C., Badham, J. A. & Kimble, B. Behavior patterns of three species of Amphibolurus (Agamidae). Copeia 1970, 497–505 (1970).Article 

Google Scholar 
50.Peters, R. A. & Ord, T. J. Display response of the Jacky Dragon, Amphibolurus muricatus (Lacertilia : Agamidae), to intruders: A semi-Markovian process. Aust. Ecol. 28, 499–506 (2003).Article 

Google Scholar 
51.Peters, R. A. & Evans, C. S. Introductory tail-flick of the Jacky dragon visual display: Signal efficacy depends upon duration. J. Exp. Biol. 206, 4293–4307 (2003).PubMed 
Article 

Google Scholar 
52.Carpenter, C. C. A comparison of the patterns of display of Urosaurus, Uta, and Streptosaurus. Herpetologica 18, 145–152 (1962).
Google Scholar 
53.Cogger, H. Reproductive cycles, fat body cycles and socio-sexual behaviour in the mallee dragon, Amphibolurus fordi (Lacertilia: Agamidae). Aust. J. Zool. 26, 653–672 (1978).Article 

Google Scholar 
54.Garcia, J. E., Rohr, D. & Dyer, A. G. Trade-off between camouflage and sexual dimorphism revealed by UV digital imaging: The case of Australian Mallee dragons (Ctenophorus fordi). J. Exp. Biol. 216, 4290–4298 (2013).PubMed 
Article 

Google Scholar 
55.Ramos, J. A. & Peters, R. A. Dragon wars: Movement-based signalling by Australian agamid lizards in relation to species ecology. Aust. Ecol. 41, 302–315 (2016).Article 

Google Scholar 
56.Gibbons, J. R. H. Comparative ecology and behaviour of lizards of the Amphibolurus decresii species complex. PhD dissertation, University of Adelaide, Adelaide, South Australia (1977).57.McLean, C. A., Moussalli, A., Sass, S. & Stuart-Fox, D. Taxonomic assessment of the Ctenophorus decresii complex (Reptilia: Agamidae) reveals a new species of dragon lizard from western New South Wales. Rec. Aust. Mus. 65, 51–63 (2013).Article 

Google Scholar 
58.Osborne, L. Information content of male agonistic displays in the territorial tawny dragon (Ctenophorus decresii). J. Ethol. 23, 189–197 (2005).Article 

Google Scholar 
59.Gibbons, J. R. The hind leg pushup display of the Amphibolurus decresii species complex (Lacertilia: Agamidae). Copeia 1979, 29–40 (1979).Article 

Google Scholar 
60.Chouinard-Thuly, L. et al. Technical and conceptual considerations for using animated stimuli in studies of animal behavior. Curr. Zool. 63, 5–19 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
61.Akagi, Y. & Kitajima, K. Computer animation of swaying trees based on physical simulation. Comput. Graph. 30, 529–539 (2006).Article 

Google Scholar 
62.Itti, L., Dhavale, N. & Pighin, F. Realistic avatar eye and head animation using a neurobiological model of visual attention. In Proc. SPIE 48th Annual International Symposium on Optical Science and Technology Vol. 5200 (eds Bosacchi, B. et al.) 64–78 (SPIE Press, Bellingham, 2003).
Google Scholar 
63.Fleishman, L. J. & Pallus, A. C. Motion perception and visual signal design in Anolis lizards. Proc. R. Soc. B. 277, 3547–3554 (2010).PubMed 
Article 

Google Scholar 
64.Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O’Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs, E. & Wagner, H. (2019). Vegan: Community Ecology Package. R package version 2.5-4. https://CRAN.R-project.org/package=vegan65.R Core Team. (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.66.Blamires, S. Circumduction and head bobbing in the agamid lizard Lophognathus temporalis. Herpetofauna 28, 51–52 (1998).
Google Scholar 
67.Carpenter, C. C. Aggression and social structure in iguanid lizards. In Lizard Ecology: A Symposium (ed. Milstead, W. W.) (University of Missouri Press Columbia, 1967).
Google Scholar 
68.Carpenter, C. Ritualistic social behaviors in lizards. in Behavior and Neurology of Lizards, An Interdisciplinary Colloquium, 253–267. (National Institute of Mental Health, 1978).69.Peters, R. A., Hemmi, J. & Zeil, J. Image motion environments: Background noise for movement-based animal signals. J. Comp. Physiol. A 194, 441–456 (2008).Article 

Google Scholar 
70.Hunter, M. L. & Krebs, J. R. Geographical variation in the song of the great tit (Parus major) in relation to ecological factors. J. Anim. Ecol 48, 759–785 (1979).Article 

Google Scholar 
71.Harmon, L. J., Kolbe, J. J., Cheverud, J. M. & Losos, J. B. Convergence and the multidimensional niche. Evolution 59, 409–421 (2005).PubMed 
Article 

Google Scholar 
72.Fleishman, L. J. Sensory influences on physical design of a visual display. Anim. Behav. 36, 1420–1424 (1988).Article 

Google Scholar 
73.Ord, T. J., Peters, R. A., Clucas, B. & Stamps, J. A. Lizards speed up visual displays in noisy motion habitats. Proc. R. Soc. Lond. B. Biol. Sci. 274, 1057–1062 (2007).
Google Scholar 
74.Hasson, O. Pursuit-deterrent signals: Communication between prey and predator. Trends Ecol. Evol. 6, 325–329 (1991).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
75.Hebets, E. A. & Uetz, G. W. Female responses to isolated signals from multimodal male courtship displays in the wolf spider genus Schizocosa (Araneae: Lycosidae). Anim. Behav. 57, 865–872 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Drews, C. The concept and definition of dominance in animal behaviour. Behaviour 125, 283–313. https://doi.org/10.1163/156853993X00290 (1993).Article 

Google Scholar 
2.Darwin, C.D. On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life, 140 (Murray, 1859) http://darwin-online.org.uk/content/frameset?itemID=F373&viewtype=text&pageseq=1 (Accessed 10 Feb 2021).3.Wilson, E. O. Sociobiology: The New Synthesis (Harvard University Press, 1975).
Google Scholar 
4.Jennions, M. D. & Petrie, M. Variation in mate choice and mating preferences: A review of causes and consequences. Biol. Rev. 72, 283–327. https://doi.org/10.1111/j.1469-185X.1997.tb00015.x (1997).CAS 
Article 
PubMed 

Google Scholar 
5.Wong, B. B. M. & Candolin, U. How is female mate choice affected by male competition?. Biol. Rev. 80, 559–571. https://doi.org/10.1017/S1464793105006809 (2005).Article 
PubMed 

Google Scholar 
6.Johnstone, R. A. Sexual selection, honest advertisement and the handicap principle: Reviewing the evidence. Biol Rev. 70, 1–65. https://doi.org/10.1111/j.1469-185x.1995.tb01439.x (1995).CAS 
Article 
PubMed 

Google Scholar 
7.Fedorka, K. M. & Mousseau, T. A. Material and genetic benefits of female multiple mating and polyandry. Anim. Behav. 64, 361–367. https://doi.org/10.1006/anbe.2002.3052 (2002).Article 

Google Scholar 
8.Kirkpatrick, M. & Ryan, M. The evolution of mating preferences and the paradox of the lek. Nature 350, 33–38. https://doi.org/10.1038/350033a0 (1991).ADS 
Article 

Google Scholar 
9.Bachmann, G. E. et al. Mate choice confers direct benefits to females of Anastrepha fraterculus (Diptera: Tephritidae). PLoS ONE 14, e0214698. https://doi.org/10.1371/journal.pone.0214698 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Gabor, C. R. & Halliday, T. R. Sequential mate choice by multiply mating smooth newts: Females become more choosy. Behav. Ecol. 8, 162–166. https://doi.org/10.1093/beheco/8.2.162 (1977).Article 

Google Scholar 
11.Clutton-Brock, T. Sexual selection in male and females. Science 318, 1882–1885. https://doi.org/10.1126/science.1133311 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
12.Clutton-Brock, T. Sexual selection in females. Anim. Behav. 77, 3–11. https://doi.org/10.1016/j.anbehav.2008.08.026 (2008).Article 

Google Scholar 
13.Bleu, J., Bessa-Gomes, C. & Laloi, D. Evolution of female choosiness and mating frequency: Effects of mating cost, density and sex ratio. Anim. Behav. 83, 131–136. https://doi.org/10.1016/j.anbehav.2011.10.017 (2012).Article 

Google Scholar 
14.Koyama, J. Mating pheromones: tropical dacines. In Fruit Flies: Their Biology, Natural Enemies and Control (eds Robinson, A. S. & Hooper, G.) 165–168 (Elsevier, 1989).
Google Scholar 
15.Malte, A. & Simmons, L. W. Sexual selection and mate choice. Tree. 21, 296–302. https://doi.org/10.1016/j.tree.2006.03.015 (2006).Article 

Google Scholar 
16.Benelli, G. et al. Sexual communication and related behaviours in Tephritidae: Current knowledge and potential applications for integrated pest management. J. Pest Sci. 87, 385–405. https://doi.org/10.1007/s10340-014-0577-3 (2014).Article 

Google Scholar 
17.Prokopy, R. J. Mating behavior of frugivorous Tephritidae in nature. In Proc. Symp. Fruit Fly Problems. XVI Int. Congr. Entomol. Kyoto, Japan, 37–46 (1980).18.Sivinski, J. M. & Burk, T. Reproductive and mating behaviour. In Fruit Flies: Their Biology, Natural Enemies and Control (eds Robinson, A. S. & Hooper, G.) 343–351 (Elsevier, 1989).
Google Scholar 
19.Benelli, G., Giunti, G., Canale, A. & Messing, R. Lek dynamics and cues evoking mating behavior in tephritid flies infesting soft fruits: Implications for behavior-based control tools. Appl. Entomol. Zool. 49, 363–373. https://doi.org/10.1007/s13355-014-0276-9 (2014).Article 

Google Scholar 
20.Arita, L. H. & Kaneshiro, K. Y. Sexual selection and lek behavior in the mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae). Pac. Sci. 43, 135–143 (1989).
Google Scholar 
21.Benelli, G. Aggression in Tephritidae flies: Where, when, why? Future directions for research in integrated pest management. Insects 6, 38–53. https://doi.org/10.3390/insects6010038 (2015).Article 

Google Scholar 
22.Landolt, P. J. & Hendrichs, J. Reproductive behavior of the papaya fruit fly, Toxotrypana curvicauda Gerstaecker (Diptera:Tephritidae). Ann. Entomol. Soc. Am. 76, 413–417. https://doi.org/10.1093/aesa/76.3.413 (1983).Article 

Google Scholar 
23.Robledo, N. R. & Arzuffi, R. Influence of host fruit and conspecifics on the release of sex pheromone by Toxotrypana curvicauda males (Diptera: Tephritidae). Environ. Entomol. 41, 387–391. https://doi.org/10.1603/EN11037 (2012).CAS 
Article 
PubMed 

Google Scholar 
24.Aluja, M. et al. Daily activity patterns and within-field distribution of papaya fruit flies (Diptera: Tephritidae) in Morelos and Veracruz, Mexico. Ann. Entomol. Soc. Am. 90, 505–520. https://doi.org/10.1093/aesa/90.4.505 (1997).Article 

Google Scholar 
25.Landolt, P. J. Behavior of flies in the genus Toxotrypana (Trypetinae: Toxotrypanini). In Fruit Flies (Tephritidae): Phylogeny and Evolution of Behavior (eds Aluja, M. & Norrbom, A.) 363–373 (CRC Press, 2000).
Google Scholar 
26.Jiménez-Pérez, A. & Villa-Ayala, P. Size, fecundity and gonadic maturation of Toxotrypana curvicauda (Diptera: Tephritidae). Fla. Entomol. 89, 194–198. https://doi.org/10.1653/0015-4040 (2006).Article 

Google Scholar 
27.Emlen, S. T. & Oring, L. W. Ecology, sexual selection, and the evolution of mating systems. Science 197, 215–223 (1997).ADS 
Article 

Google Scholar 
28.Landolt, P. J., Heath, R. R. & King, J. R. Behavioral responses of female papaya fruit flies, Toxotrypana curvicauda Gerstaecker (Diptera:Tephritidae), to male-produced sex pheromones. Ann. Entomol. Soc. Am. 78, 751–755. https://doi.org/10.1093/aesa/78.6.751 (1985).Article 

Google Scholar 
29.Sivinski, J. M. & Webb, J. C. The form and function of acoustic courtship signals of the papaya fruit fly, Toxotrypana curvicauda (Tephritidae). Fla. Entomol. 68, 634–664 (1985).Article 

Google Scholar 
30.Landolt, P. J. Chemical ecology of papaya fruit fly. In Fruit Flies: Biology and Management 1st edn (eds Aluja, M. & Liedo, P.) 207–210 (Springer-Verlag, 1990).
Google Scholar 
31.Castrejón, A.F. Aspectos de la biología y hábitos de Toxotrypana curvicauda Gerst. (Diptera:Tephritidae) en condiciones de laboratorio y su distribución en una plantación de Carica papaya L. en Yautepec, Morelos. Bachelors’ dissertation, Instituto Politécnico Nacional. Mexico (1987).32.Robacker, C., Mangan, R. L., Moreno, D. S. & Tarshis, A. M. Mating behavior and male mating success in wild Anastrepha ludens (Diptera: Tephritidae) on a field-caged host tree. J. Insect Behav. 4, 471–487. https://doi.org/10.1007/BF01049332 (1991).Article 

Google Scholar 
33.Taylor, P. W. & Yuval, B. Postcopulatory sexual selection in Mediterranean fruit flies: Advantages for large and protein-fed males. Anim. Behav. 58, 247–254. https://doi.org/10.1006/anbe.1999.1137 (1999).CAS 
Article 
PubMed 

Google Scholar 
34.Abraham, S. et al. Remating behavior in Anastrepha fraterculus (Diptera: Tephritidae) females is affected by male juvenile hormone analog treatment but not by male sterilization. Bull. Entomol. Res. 103, 310–317. https://doi.org/10.1017/S0007485312000727 (2013).CAS 
Article 
PubMed 

Google Scholar 
35.Sánchez-Rosario, M., Pérez-Staples, D., Toledo, J., Valle-Mora, J. & Liedo, P. Artificial selection on mating competitiveness of Anastrepha ludens for sterile insect technique application. Entomol. Exp. Appl. 162, 133–147. https://doi.org/10.1111/eea.12540 (2017).Article 

Google Scholar 
36.Colwell, A. E. & Shorey, H. H. The courtship behavior of the house fly, Musca domestica (Diptera: Muscidae). Ann. Entomol. Soc. Am. 68, 152–156. https://doi.org/10.1093/aesa/68.1.152 (1975).Article 

Google Scholar 
37.Yeh, S. D., Liou, S. R. & True, J. Genetics of divergence in male wing pigmentation and courtship behavior between Drosophila elegans and D. gunungcola. Heredity 96, 383–395. https://doi.org/10.1038/sj.hdy.6800814 (2006).Article 
PubMed 

Google Scholar 
38.Benelli, G. & Romano, D. Looking for the right mate—What do we really know on the courtship and mating of Lucilia sericata (Meigen)?. Acta Trop. https://doi.org/10.1016/j.actatropica.2018.08.013 (2019).Article 
PubMed 

Google Scholar 
39.Wicker-Thomas, C. Pheromonal communication involved in courtship behavior in Diptera. J. Insect Physiol. 53, 1089–1100. https://doi.org/10.1016/j.jinsphys.2007.07.003 (2007).CAS 
Article 
PubMed 

Google Scholar 
40.Tadeo, E., Aluja, M. & Rull, J. Alternative mating tactics as potential prezygotic barriers to gene flow between two sister species of frugivorous fruit flies. J. Insect Behav. 26, 708–720. https://doi.org/10.1007/s10905-013-9383-7 (2013).Article 

Google Scholar 
41.Burk, T. & Webb, J. C. Effect of male size on calling propensity, song parameters, and mating success in Caribean fruit flies (Anastrepha suspensa (Loew)). Ann. Entomol. Soc. Am. 76, 678–682. https://doi.org/10.1093/aesa/76.4.678 (1983).Article 

Google Scholar 
42.Briceño, R. D. & Eberhard, W. G. Possible Fisherian changes in female mate-choice criteria in a mass-reared strain of Ceratitis capitata (Diptera: Tephritidae). Ann. Entomol. Soc. Am. 93, 343–345. https://doi.org/10.1603/0013-8746(2000)093[0343:PFCIFM]2.0.CO;2 (2000).Article 

Google Scholar 
43.Poramarcom, R. & Boake, C. R. B. Behavioural influences on male mating success in the Oriental fruit fly, Dacus dorsalis Hendel. Anim. Behav. 42, 453–460. https://doi.org/10.1016/S0003-3472(05)80044-2 (1991).Article 

Google Scholar 
44.Dukas, R. & Scott, A. Fruit fly courtship: The female perspective. Curr. Zool. 61, 1008–1014. https://doi.org/10.1093/czoolo/61.6.1008 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
45.Benelli, G. et al. Contest experience enhances aggressive behaviour in a fly: When losers learn to win. Sci. Rep. 5, 9347. https://doi.org/10.1038/srep09347 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
46.Landolt, P. J. Reproductive maturation and premating period of the papaya fruit fly Toxotrypana curvicauda (Diptera: Tephritidae). Fla. Entomol. 67, 240–244 (1984).Article 

Google Scholar 
47.Martínez Rogelio. Comportamiento agonista de Toxotrypana curvicauda Gerstaecker (Diptera:Tephritidae). Bachelors dissertation, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos, Mexico, (2016)48.Arzuffi, A. Factores determinantes del orden jerárquico en el acocil Cambarellus zempoalensis (Crustacea: Cambaridae). Doctorate dissertation, Instituto Politécnico Nacional. Mexico City (1997).49.Martin, P. & Bateson, P. Measuring Behaviour: An Introductory Guide (Cambridge University Press, 2007). https://doi.org/10.1002/ajpa.1330740314.
Google Scholar 
50.Markow, T. Behavioral and sensory basis of courtship success in Drosophila melanogaster. PNAS 84, 6200–6204. https://doi.org/10.1073/pnas.84.17.6200 (1987).ADS 
CAS 
Article 
PubMed 

Google Scholar 
51.Arzuffi, B.A., Salazar-Marcial, L. & Robledo, Q.N. Cortejo y apareamiento de Toxotrypana curvicauda (Diptera:Tephritidae): análisis cuantitativo y efecto de la edad. Primer Congreso Internacional de Agronomía Tropical y Segundo Simposio Nacional Agroalimentario. Villahermosa, Tabasco, México (2009).52.Salazar-Marcial, L., Arzuffi, B. R. & Robledo, Q. N. Efecto de la edad sobre el cortejo y el apareamiento en Toxotrypana curvicauda (Diptera:Tephritidae). Entomol. Mex. 9, 328–329 (2010).
Google Scholar  More

The polysaccharide xylan limits the growth of C. crescentus cells compared to the monomer xylose in well-mixed environmentsWe first tested our hypothesis that in well-mixed conditions the polymer xylan would limit the productivity of microbial populations relative to the monomer xylose. To determine if this was the case, we grew C. crescentus cells in the same concentration (weight/volume) of either the polymer xylan or its monomeric constituent xylose, both provided as the sole carbon source (Fig. 1a). We then compared the maximum growth rate and the maximal population size over the course of a 54 h growth cycle (Fig. 1b). In line with expectations, populations growing on the monomer xylose achieved higher growth rates and a higher maximum population size (Fig. 1b–d). This was true for all concentrations (0.01–0.1%) of monomer and polymer tested (Supplementary Fig. 2). These findings suggest that in well-mixed environments of equal carbon concentration, the complexity of the growth substrate governs the growth of C. crescentus populations.Cells engage in colonial behaviors on xylan whereas they exhibit solitary behaviors on xyloseGroup formation could be a key mechanism through which cells could overcome polymer-induced growth limitations that exist in well-mixed environments. Collective behavior would allow cells to increase their local cell density, which leads to higher local concentrations of the monomeric products of polymer degradation. To test this prediction, we tested whether xylose and xylan elicit different behavioral responses in C. crescentus. We used microfluidic growth chambers in which cells were forced to grow as a monolayer. Our expectation was that growth within these devices would provide the spatial structure to overcome the growth limitations observed in well-mixed conditions (Supplementary Fig. 1). We tracked and quantified movement, and growth of individual cells using time-lapse microscopy and image analysis. Chambers were constantly replenished with minimal medium containing either xylose or xylan through a main nutrient feeding channel, as described elsewhere [20, 23, 24].We found that C. crescentus displayed strikingly disparate behaviors in xylan and xylose: cells formed microcolonies on the polymer xylan (Fig. 2a, Supplementary Video 1), whereas on the monomer xylose they did not (Fig. 2b, Supplementary Video 2). We analyzed the temporal dynamics of cell growth and movement in the two carbon sources by following individual cells using cell segmentation and tracking. Mapping the lineages based on division events for all the cells in a chamber revealed that the microcolonies on the polymer xylan originated from a single progenitor cell (Fig. 2d, Supplementary Fig. 3a–c; Supplementary Video 3). This finding indicates that microcolonies were a result of swarmer cells not dispersing after division, rather than a product of secondary aggregation by planktonic cells. In contrast, in the monomer xylose only the stalked cells remained in the same position after cell division, whereas the presumably flagellated swarmer cells moved away (Fig. 2e, Supplementary Fig. 4a–c). As a consequence of this difference in behavior, the number of sessile cells increased much more rapidly in xylan. The number of cells in a growth chamber doubled on average every 3.6 ± 0.54 h in xylan (mean ± 95% CI, Fig. 2c) but took 15.50 ± 7.55 h to double in xylose (mean ± 95% CI, Fig. 2c). These differences occurred despite a similar propensity to produce offspring per sessile cell in the two substrates (Supplementary Fig. 5), and thus were driven by the reduced rate at which cells dispersed in xylan.Fig. 2: Cells display solitary behavior on xylose and aggregative behavior on xylan.Representative images of C. crescentus CB15 cells (labeled with constitutively expressed mKate2, false colored as magenta) at different time points within the microfluidic growth chambers supplied with either xylan (a) or xylose (b) as the sole source of carbon. c On xylan (yellow), the number of sessile cells in the growth chamber increases with time, whereas on xylose (blue) it remains nearly constant. Squares indicate the number of cells present at a given time point in each chamber (nchambers = 9), with a linear or exponential regression line for each chamber (xylose, linear regression model, R2 = 0.69–0.92, slope = 1.22–3.27, P  More

A 2-scale ensemble machine learningPredictions of soil nutrients are based on a fully automated and fully optimized 2-scale Ensemble Machine Learning (EML) framework as implemented in the mlr package for Machine Learning (https://mlr.mlr-org.com/). The entire process can be summarized in the following eight steps (Fig. 7):

1.

Prepare point data, quality control all values and remove any artifacts or types.

2.

Upload to Google Earth Engine, overlay the point data with the key covariates of interest and test fitting random forest or similar to get an initial estimate of relative variable importance and pre-select features of interest.

3.

Decide on a final list of all covariates to use in predictions, prepare covariates for predictive modeling—either using Amazon AWS or similar. Quality control all 250 m and 30 m resolution covariates and prepare Analysis-Ready data in a tiling system to speed up overlay and prediction.

4.

Run spatial overlay using 250 m and 30 m resolution covariates and generate regression matrices.

5.

Fit 250 m and 30 m resolution Ensemble Machine Learning models independently per soil property using spatial blocks of 30–100 km. Run sequentially: model fine-tuning, feature selection and stacking. Generate summary accuracy assessment, variable importance, and revise if necessary.

6.

Predict 250 m and 30 m resolution tiles independently using the optimized models. Downscale the 250 m predictions to 30 m resolution using Cubicsplines (GDAL).

7.

Combine predictions using Eq. (3) and generate pooled variance/s.d. using Eq. (4).

8.

Generate all final predictions as Cloud-Optimized GeoTIFFs. Upload to the server and share through API/Geoserver.

Figure 7Scheme: a two-scale framework for Predictive Soil Mapping based on Ensemble Machine Learning (as implemented in the mlr and mlr3 frameworks for Machine Learning28 and based on the SuperLearner algorithm). This process is applied for a bulk of soil samples, the individual models per soil variable are then fitted using automated fine-tuning, feature selection and stacking. The map is showing distribution of training points used in this work. Part of the training points that are publicly available are available for use from https://gitlab.com/openlandmap/compiled-ess-point-data-sets/.Full size imageFor the majority of soil properties, excluding depth to bedrock, we also use soil depth as one of the covariates so that the final models for the two scales are in the form5:$$begin{aligned} y(phi ,theta ,d) = d + x_1 (phi ,theta ) + x_2 (phi ,theta ) + cdots + X_p (phi ,theta ) end{aligned}$$
(1)
where y is the target variable, d is the soil sampling depth, (phi theta) are geographical coordinates (northing and easting), and (X_p) are the covariates. Adding soil depth as a covariate allows for directly producing 3D predictions35, which is our preferred approach as prediction can be then produced at any depth within the standard depth interval (e.g. 0–50 cm).Ensemble machine learningEnsembles are predictive models that combine predictions from two or more learners36. We implement ensembling within the mlr package by fitting a ‘meta-learner’ i.e. a learner that combines all individual learners. mlr has extensive functionality, especially for model ‘stacking’ i.e. to generate ensemble predictions, and also incorporates spatial Cross-Validation37. It also provides wrapper functions to automate hyper-parameter fine-tuning and feature selection, which can all be combined into fully-automated functions to fit and optimize models and produce predictions. Parallelisation can be initiated by using the parallelMap package, which automatically determines available resources and cleans-up all temporary sessions38.For stacking multiple base learners we use the SuperLearner method39, which is the most computational method but allows for an independent assessment of all individual learners through k-fold cross validation with refitting. To speed up computing we typically use a linear model (predict.lm) as the meta-learner, so that in fact the final formula to derive the final ensemble prediction can be directly interpreted by printing the model summary.The predictions in the Ensemble models described in Fig. 7 are in principle based on using the following five Machine Learning libraries common for many soil mapping projects5.

1.

Ranger: fully scalable implementation of Random Forest23.

2.

XGboost: extreme gradient boosting40.

3.

Deepnet: the Open Source implementation of deep learning26.

4.

Cubist: the Open Source implementation of Cubist regression trees41.

5.

Glmnet: GLM with Lasso or Elasticnet Regularization24.

These Open source libraries, with the exception of the Cubist, are available through a variety of programming environments including R, Python and also as standalone C++ libraries.Merging coarse and fine-scale predictionsThe idea of modeling soil spatial variation at different scales can be traced back to the work of McBratney42. In a multiscale model, soil variation can be considered a composite signal (Fig. 8):$$begin{aligned} y({mathbf{s}}_{mathtt {B}}) = S_4({mathbf{s}}_{mathtt {B}}) + S_3({mathbf{s}}_{mathtt {B}}) + S_2({mathbf{s}}_{mathtt {B}}) + S_1({mathbf{s}}_{mathtt {B}}) + varepsilon end{aligned}$$
(2)
where (S_4) is the value of the target variable estimated at the coarsest scale, (S_3), (S_2) and (S_1) are the higher order components, ({mathbf{s}}_{mathtt {B}}) is the location or block of land, and (varepsilon) is the residual soil variation i.e. pure noise.Figure 8Decomposition of a signal of spatial variation into four components plus noise. Based on McBratney42. See also Fig. 13 in Hengl et al.21.Full size imageIn this work we used a somewhat simplified version of Eq. (2) with only two scale-components: coarse ((S_2); 250 m) and fine ((S_1); 30 m). We produce the coarse-scale and fine-scale predictions independently, then merge using a weighted average43:$$begin{aligned} {hat{y}}({mathbf{s}}_{mathtt {B}}) = frac{sum _{i=1}^{2}{ w_i cdot S_i({mathbf{s}}_{mathtt {B}})}}{sum _{i=1}^{2}{ w_i }}, ; ; w_i = frac{1}{sigma _{i,mathrm{CV}}^2} end{aligned}$$
(3)
where ({hat{y}}({mathbf{s}}_{mathtt {B}})) is the ensemble prediction, (w_i) is the model weight and (sigma _{i,mathrm{CV}}^2) is the model squared prediction error obtained using cross-validation. This is an example of Ensemble Models fitted for coarse-scale model for soil pH:and the fine-scale model for soil pH:Note that in this case the coarse-scale model is somewhat more accurate with (mathrm {RMSE}=0.463), while the 30 m covariates achieve at best (mathrm {RMSE}=0.661), hence the weights for 250 m model are about 2(times) higher than for the 30 m resolution models. A step-by-step procedure explaining in detail how the 2-scale predictions are derived and merged is available at https://gitlab.com/openlandmap/spatial-predictions-using-eml. An R package landmap44 that implements the procedure in a few lines of code is also available.Transformation of log-normally distributed nutrients and propertiesFor the majority of log-normal distributed (right-skewed) variables we model and predict the ln-transformed values ((log _e(x+1))), then provide back-transformed predictions ((e^{x}-1)) to users via iSDAsoil. Note that also pH is a log-transformed variable of the hydrogen ion concentrations.Although ln-transformation is not required for non-linear models such as Random Forest or Gradient Boosting, we decided to apply it to give proportionally higher weights to lower values. This is, in principle, a biased decision by us the modelers as our interest is in improving predictions of critical values for agriculture i.e. producing maps of nutrient deficiencies and similar (hence focus on smaller values). If the objective of mapping was to produce soil organic carbon of peatlands or similar, then the ln-transformation could have decreased the overall accuracy, although with Machine Learning models sometimes it is impossible to predict effects as they are highly non-linear.Derivation of prediction errorsWe also provide per-pixel uncertainty in terms of prediction errors or prediction intervals (e.g. 50%, 68% and/or 90% probability intervals)45. Because stacking of learners is based on repeated resampling, the prediction errors (per pixel) can be determined using either:

1.

Quantile Regression Random Forest46, in our case by using the 4–5 base learners,

2.

Simplified procedure using Bootstraping, then deriving prediction errors as standard deviation from multiple independently fitted learners1.

Both are non-parametric techniques and the prediction errors do not require any assumptions or initial parameters, but come at a cost of extra computing. By default, we provide prediction errors with a probability of 67%, which is the 1 standard deviation upper and lower prediction interval. Prediction errors indicate extrapolation areas and should help users minimize risks of taking decisions.For derivation of prediction interval via either Quantile Regression RF or bootstrapping, it is important to note that the individual learners must be derived using randomized subsets of data (e.g. fivefold) which are spatially separated using block Cross-Validation or similar, otherwise the results might be over-optimistic and prediction errors too narrow.Figure 9Schematic example of the derivation of a pooled variance ((sigma _{mathtt {250m+30m}})) using the 250 m and 30 m predictions and predictions errors with (a) larger and (b) smaller differences in independent predictions.Full size imageFurther, the pooled variance (({hat{sigma }}_E)) from the two independent models (250 m and 100 m scales in Fig. 7) can be derived using47:$$begin{aligned} {hat{sigma }}_E = sqrt{sum _{j=1}^{s}{w_j cdot (hat{sigma }_j^2+{hat{mu }}_j^2 )} – left( sum _{j=1}^{s}{w_j cdot {hat{mu }}_j} right) ^2 }, ; ; sum _{j=1}^{s}{w_j} = 1 end{aligned}$$
(4)
where (sigma _j^2) is the prediction error for the independent components, ({hat{mu }}_j) is the predicted value, and w are the weights per predicted component (need to sum up to 1). If the two independent models (250 m and 30 m) produce very similar predictions so that ({hat{mu }}_{mathtt {250}} approx {hat{mu }}_{mathtt {30}}), then the pooled variance approaches the geometric mean of the two variances; if the independent predictions are different (({hat{mu }}_{mathtt {250}} – {hat{mu }}_{mathtt {30}} > 0)) than the pooled variances increase proportionally to this additional difference (Fig. 9).Accuracy assessment of final mapsWe report overall average accuracy in Table 1 and Fig. 4 using spatial fivefold Cross-Validation with model refitting1,48. For each variable we then compute the following three metrics: (1) Root Mean Square Error, (2) R-square from the meta-learner, and (3) Concordance Correlation Coefficient (Fig. 4), which is derived using49:$$begin{aligned} rho _c = frac{2 cdot rho cdot sigma _{{{hat{y}}}} cdot sigma _y }{ sigma _{{{hat{y}}}}^2 + sigma _y^2 + (mu _{{{hat{y}}}} – mu _y)^2} end{aligned}$$
(5)
where ({{hat{y}}}) are the predicted values and y are actual values at cross-validation points, (mu _{{{hat{y}}}}) and (mu _y) are predicted and observed means and (rho) is the correlation coefficient between predicted and observed values. CCC is the most appropriate performance criteria when it comes to measuring agreement between predictions and observations.For Cross-validation we use the spatial tile ID produced in the equal-area projection system for Africa (Lambert Azimuthal EPSG:42106) as the blocking parameter in the training function in mlr. This ensures that points falling in close proximity ( More

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript. More

1.Wilson, E. O. Sociobiology (Harvard Press, 1975).
Google Scholar 
2.Hölldobler, B. & Lumsden, C. J. Territorial strategies in ants. Science 210, 732–739 (1980).MathSciNet 
PubMed 
MATH 
Article 
ADS 
PubMed Central 

Google Scholar 
3.Baker, R. R. Insect territoriality. Annu. Rev. Entomol. 28, 65–89 (1983).Article 

Google Scholar 
4.Christensen, C. & Radford, A. N. Dear enemies or nasty neighbors? Causes and consequences of variation in the responses of group-living species to territorial intrusions. Behav. Ecol. 29, 1004–1013 (2018).Article 

Google Scholar 
5.Fisher, J. B. Evolution and bird sociality. In Evolution as a process (eds. Huxley, J., Hardy, A. C. & Ford, E. B.) 71–83. (Allen & Unwin, Australia, 1954).6.Temeles, E. J. The role of neighbours in territorial systems: when are they “dear enemies”?. Anim. Behav. 47, 339–350 (1994).Article 

Google Scholar 
7.Adams, E. S. Territoriality in ants (Hymenoptera: Formicidae): a review. Myrmecol. News 23, 101–118 (2016).
Google Scholar 
8.Müller, C. A. & Manser, M. B. “Nasty neighbours” rather than “dear enemies” in a social carnivore. Proc. R Soc. B Biol. Sci. 274, 959–965 (2007).Article 

Google Scholar 
9.Tanner, C. J. & Adler, F. R. To fight or not to fight: context-dependent interspecific aggression in competing ants. Anim. Behav. 77, 297–305 (2009).Article 

Google Scholar 
10.Mabelis, A. A. Wood ant wars. Neth. J. Zool. 29, 451–620 (1979).Article 

Google Scholar 
11.Hölldobler, B. Recruitment behavior, home range orientation and territoriality in harvester ants, Pogonomyrmex. Behav. Ecol. Sociobiol. 1, 3–44 (1976).Article 

Google Scholar 
12.Hölldobler, B. Tournaments and slavery in a desert ant. Science 80(192), 912–914 (1976).Article 
ADS 

Google Scholar 
13.Carlin, N. F. & Hölldobler, B. The kin recognition system of carpenter ants (Camponotus spp.) – I. Hierarchical cues in small colonies. Behav. Ecol. Sociobiol. 19, 123–134 (1986).14.Carlin, N. F. & Hölldobler, B. The kin recognition system of carpenter ants (Camponotus spp.)—II. Larger colonies. Behav. Ecol. Sociobiol. 20, 209–217 (1987).Article 

Google Scholar 
15.Langen, T. A., Tripet, F. & Nonacs, P. The red and the black: habituation and the dear-enemy phenomenon in two desert Pheidole ants. Behav. Ecol. Sociobiol. 48, 285–292 (2000).Article 

Google Scholar 
16.Dimarco, R. D., Farji-Brener, A. G. & Premoli, A. C. Dear enemy phenomenon in the leaf-cutting ant Acromyrmex lobicornis: behavioral and genetic evidence. Behav. Ecol. 21, 304–310 (2010).Article 

Google Scholar 
17.Yagound, B., Crowet, M., Leroy, C., Poteaux, C. & Châline, N. Interspecific variation in neighbour–stranger discrimination in ants of the Neoponera apicalis complex. Ecol. Entomol. 42, 125–136 (2017).Article 

Google Scholar 
18.Benedek, K. & Kóbori, O. T. “Nasty neighbour” effect in Formica pratensis retz. (Hymenoptera: Formicidae). N. West J. Zool. 10, 245–250 (2014).
Google Scholar 
19.Newey, P. S., Robson, S. K. A. & Crozier, R. H. Know thine enemy: why some weaver ants do but others do not. Behav. Ecol. 21, 381–386 (2010).Article 

Google Scholar 
20.Sanada-Morimura, S. et al. Encounter-induced hostility to neighbors in the ant Pristomyrmex pungens. Behav. Ecol. 14, 713–718 (2003).Article 

Google Scholar 
21.Boulay, R., Cerdá, X., Simon, T., Roldan, M. & Hefetz, A. Intraspecific competition in the ant Camponotus cruentatus: should we expect the “dear enemy” effect?. Anim. Behav. 74, 985–993 (2007).Article 

Google Scholar 
22.Frizzi, F. et al. The rules of aggression: How genetic, chemical and spatial factors affect intercolony fights in a dominant species, the mediterranean acrobat ant Crematogaster scutellaris. PLoS ONE 10, 1–16 (2015).Article 
CAS 

Google Scholar 
23.Crosland, M. W. Kin recognition in the ant Rhytidoponera confusa I. Environmental odour. Anim. Behav. 37, 912–919 (1989).Article 

Google Scholar 
24.Beye, M., Neumann, P. & Moritz, R. F. A. Nestmate recognition and the genetic gestalt in the mound-building ant Formica polyctena. Insectes Soc. 44, 49–58 (1997).Article 

Google Scholar 
25.Beye, M., Neumann, P., Chapuisat, M., Pamilo, P. & Moritz, R. F. A. Nestmate recognition and the genetic relatedness of nests in the ant Formica pratensis. Behav. Ecol. Soc. 43, 67–72 (1998).Article 

Google Scholar 
26.Martin, S. & Drijfhout, F. A review of ant cuticular hydrocarbons. J. Chem. Ecol. 35, 1151–1161 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Rico-Gray, V., Oliveira, P. S. & Oliveira, P. S. The Ecology and Evolution of Ant-plant Interactions (University of Chicago Press, 2007).
Google Scholar 
28.Adams, E. S. Boundary disputes in the territorial ant Azteca trigona: effects of asymmetries in colony size. Anim. Behav. 39, 321–328 (1990).Article 

Google Scholar 
29.Adams, E. S. Territory defense by the ant Azteca trigona: maintenance of an arboreal ant mosaic. Oecologia 97, 202–208 (1994).PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
30.Frederickson, M. E. & Gordon, D. M. The intertwined population biology of two Amazonian myrmecophytes and their symbiotic ants. Ecology 90, 1595–1607 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Heil, M. & McKey, D. Protective and in ecological model systems in ecological and evolutionary research. Annu. Rev. Ecol. Evol. Syst. 34, 425–453 (2003).Article 

Google Scholar 
32.Hölldobler, B. The chemistry of social regulation: Multicomponent signals in ant societies. Proc. Natl. Acad. Sci. USA 92, 19–22 (1995).PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
33.Howard, R. W. & Blomquist, G. J. Ecological, behavioral, and biochemical aspects of insect hydrocarbons. Annu. Rev. Entomol. 50, 371–393 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Boulay, R., Hefetz, A., Soroker, V. & Lenoir, A. Camponotus fellah colony integration: worker individuality necessitates frequent hydrocarbon exchanges. Anim. Behav. 59, 1127–1133 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
35.Errard, C., Hefetz, A. & Jaisson, P. Social discrimination tuning in ants: template formation and chemical similarity. Behav. Ecol. Sociobiol. 59, 353–363 (2006).Article 

Google Scholar 
36.Brandstaetter, A. S., Rössler, W. & Kleineidam, C. J. Friends and foes from an ant brain’s point of view—neuronal correlates of Colony Odors in a social insect. PLoS ONE 6, e21383 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
37.Leonhardt, S. D., Brandstaetter, A. S. & Kleineidam, C. J. Reformation process of the neuronal template for nestmate-recognition cues in the carpenter ant Camponotus floridanus. J. Comp. Physiol. 193, 993–1000 (2007).Article 

Google Scholar 
38.Guerrieri, F. J. et al. Ants recognize foes and not friends. Proc. R Soc. B Biol. Sci. 276, 2461–2468 (2009).CAS 
Article 

Google Scholar 
39.Newey, P. Not one odour but two: a new model for nestmate recognition. J. Theor. Biol. 270, 7–12 (2011).PubMed 
Article 

Google Scholar 
40.Martin, S. J., Vitikainen, E., Drijfhout, F. P. & Jackson, D. Conspecific ant aggression is correlated with chemical distance, but not with genetic or spatial distance. Behav. Gen. 42, 323–331 (2012).Article 

Google Scholar 
41.Longino, J. T. Azteca ants in Cecropia trees: taxonomy, colony structure, and behavior. In Ant-Plant Interactions (eds Huxley, C. R. & Cutler, D. F.) 271–288 (Oxford University Press, 1991).
Google Scholar 
42.Schupp, E. W. Azteca protection of Cecropia: ant occupation benefits juvenile trees. Oecologia 70, 379–385 (1986).PubMed 
Article 
ADS 

Google Scholar 
43.Oliveira, K. N. et al. The effect of symbiotic ant colonies on plant growth: a test using an Azteca-Cecropia system. PLoS ONE 10, 1–13 (2015).
Google Scholar 
44.Silva, C. A., Vieira, M. F. & Amaral, C. H. Floral attributes, ornithophily and reproductive success of Palicourea longepedunculata (Rubiaceae), a distylous shrub in southeastern Brazil. Rev. Bras. Bot. 33, 207–210 (2010).Article 

Google Scholar 
45.Veloso, H. P., Rangel Filho, A. L. R. & Lima, J. C. A. Classificação da Vegetação Brasileira Adaptada a um Sistema Universal (Ibge, 1991).46.Berg, C. C., Rosselli, P. F. & Davidson, D. W. Cecropia. Flora Neotropica. 94, 1–230 (2005). Retrieved April 22, 2020, from www.jstor.org/stable/439393847.Emery, C. & de Voyage, M. M. Bedot et Pictel dans l’Archipel Malais. Formicides de l’Archipel Malais [Travel of MM. Bedot and Pictel in the Malaysian Archipelago. Formicides from the Malaysian Archipelago]. Rev. Suisse. Zool. 1, 187–229 (1893).Article 

Google Scholar 
48.Davidson, D. W. & Fisher, B. L. Symbiosis of ants with Cecropia as a function of light regime. In Ant-Plant Interactions (eds. Huxley, C. R. & Cutler, D. F.) 289–309 (Oxford University Press, UK, 1991).49.Davidson, D. W. & McKey, D. Ant-plant symbioses: stalking the chuyachaqui. Trends Ecol. Evol. 8, 326–332 (1993).CAS 
PubMed 
Article 

Google Scholar 
50.Fonseca, C. R. & Ganade, G. Asymmetries, compartments and null interactions in an Amazonian ant-plant community. J. Anim. Ecol. 65, 339–347 (1996).Article 

Google Scholar 
51.Fonseca, C. R. Amazonian ant-plant interactions and the nesting space limitation hypothesis. J. Trop. Ecol. 15, 807–825 (1999).Article 

Google Scholar 
52.Longino, J. T. Geographic variation and community structure in an ant-plant mutualism: Azteca and Cecropia in Costa Rica. Biotropica 21, 126–132 (1989).Article 

Google Scholar 
53.Bruna, E. M., Izzo, T. J., Inouye, B. D., Uriarte, M. & Vasconcelos, H. L. Asymmetric dispersal and colonization success of Amazonian plant-ants queens. PLoS ONE 6, e22937 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
54.Yu, D. W. et al. Experimental demonstration of species coexistence enabled by dispersal limitation. J. Anim. Ecol. 73, 1102–1114 (2004).Article 

Google Scholar 
55.Rocha, C. F. D. & Bergallo, H. G. Bigger ant colonies reduce herbivory and herbivore residence time on leaves of an ant-plant: Azteca muelleri vs. Coelomera ruficornis on Cecropia pachystachya. Oecologia 91, 249–252 (1992).PubMed 
Article 
ADS 

Google Scholar 
56.Campbell, H., Fellowes, M. D. E. & Cook, J. M. Arboreal thorn-dwelling ants coexisting on the savannah ant-plant, Vachellia erioloba, use domatia morphology to select nest sites. Insectes Soc. 60, 373–382 (2013).Article 

Google Scholar 
57.Marting, P. R., Wcislo, W. T. & Pratt, S. C. Colony personality and plant health in the Azteca-Cecropia mutualism. Behav. Ecol. 29, 264–271 (2018).Article 

Google Scholar 
58.Tschinkel, W. R. Sociometry and sociogenesis of colonies of the fire ant Solenopsis invicta during one annual cycle: ecological archives M063–002. Ecol. Monogr. 63, 425–457 (1993).Article 

Google Scholar 
59.Wills, B. D., Powell, S., Rivera, M. D. & Suarez, A. V. Correlates and consequences of worker polymorphism in ants. Ann. Rev. Entomol. 63, 575–598 (2018).CAS 
Article 

Google Scholar 
60.Holway, D. A., Suarez, A. V. & Case, T. J. Loss of intraspecific aggression in the success of a widespread invasive social insect. Science 282, 949–952 (1998).CAS 
PubMed 
Article 
ADS 

Google Scholar 
61.Giraud, T., Pedersen, J. S. & Keller, L. Evolution of supercolonies: the Argentine ants of southern Europe. PNAS 99, 6075–6079 (2002).CAS 
PubMed 
Article 
ADS 

Google Scholar 
62.Fischer, D. C. Fundamentos de cromatografia. Rev. Bras. Cienc. Farm. 42, 308–308 (2006).
Google Scholar 
63.Koo, I., Shi, X., Kim, S. & Zhang, X. IMatch2: Compound identification using retention index for analysis of gas chromatography-mass spectrometry data. J. Chromatogr. A 1337, 202–210 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.El-Sayed, A. M. The Pherobase: Database of Pheromones and Semiochemicals. https://www.pherobase.com. Accessed 11 July 2020 (2020).65.NIST Livro de Química na Web. Base de dados de Referência padrão do NIST número 69. http://webbook.nist.gov/chemistry/. Accessed 13 July 2020 (2016).66.Vidal, D. M., Fávaro, C. F., Guimaraes, M. M. & Zarbin, P. H. Identification and synthesis of the male-produced sex pheromone of the soldier beetle Chauliognathus fallax (Coleoptera: Cantharidae). J. Brazil. Chem. Soc. 27, 1506–1511 (2016).CAS 

Google Scholar 
67.Carlson, D. A., Bernier, U. R. & Sutton, B. D. Elution patterns from capillary GC for methyl-branched alkanes. J. Chem. Ecol. 24, 1845–1865 (1998).CAS 
Article 

Google Scholar 
68.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org. Accessed 16 June 2020 (2017).69.Lanan, M. C. & Bronstein, J. L. An ant’s-eye view of an ant-plant protection mutualism. Oecologia 172, 779–790 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
70.Briefer, E., Rybak, F. & Aubin, T. When to be a dear enemy: flexible acoustic relationships of neighbouring skylarks Alauda arvensis. Anim. Behav. 76, 1319–1325 (2008).Article 

Google Scholar 
71.Hyman, J. Seasonal variation in response to neighbors and strangers by a territorial songbird. Ethology 111, 951–961 (2005).Article 

Google Scholar 
72.Sturgis, S. J. & Gordon, D. M. Nestmate recognition in ants (Hymenoptera: Formicidae): a review. Myrmecol. News 16, 101–110 (2012).
Google Scholar 
73.Matthews, R. W. & Matthews, J. R. Insect Behavior (Springer, 2009).
Google Scholar 
74.Boucher, D. H., James, S. & Keeler, K. H. The ecology of mutualism. Annu. Rev. Ecol. Evol. Syst. 13, 315–347 (1982).Article 

Google Scholar 
75.Connor, R. C. The benefits of mutualism: a conceptual framework. Biol. Rev. 70, 427–457 (1995).Article 

Google Scholar 
76.Bronstein, J. L. The costs of mutualism. Am. Zool. 41, 825–839 (2001).
Google Scholar 
77.Hölldobler, B. & Wilson, E. O. The Ants (Harvard University Press, 1990).
Google Scholar 
78.Dejean, A., Corbara, B., Orivel, J. & Leponce, M. Rainforest canopy ants: the implications of territoriality and predatory behavior. Funct. Ecol. Commun. 1, 105–120 (2007).
Google Scholar 
79.Dejean, A., Grangier, J., Leroy, C. & Orivel, J. Predation and aggressiveness in host plant protection: a generalization using ants from the genus Azteca. Naturwissenschaften 96, 57–63 (2009).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
80.Tripovich, J. S., Charrier, I., Rogers, T. L., Canfield, R. & Arnould, J. P. Acoustic features involved in the neighbour-stranger vocal recognition process in male Australian fur seals. Behav. Process. 79, 74–80 (2008).CAS 
Article 

Google Scholar 
81.Favaro, L., Gamba, M., Gili, C. & Pessani, D. Acoustic correlates of body size and individual identity in banded penguins. PLoS ONE 12, e0170001 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
82.Heinze, J., Foitzik, S., Hippert, A. & Hölldobler, B. Apparent dear-enemy phenomenon and environment-based recognition cues in the ant Leptothorax nylanderi. Ethology 102, 510–522 (1996).Article 

Google Scholar 
83.Vander Meer, R. K. & Morel, L. Nestmate Recognition in Ants. 79–103 (Pheromone communication in Soc. Insects, 1998).84.Provost, E., Blight, O., Tirard, A. & Renucci, M. Hydrocarbons and insects’ social physiology. Insect Physiology: New Research 19–72 (2008).85.Crozier, R. H. & Dix, M. W. Analysis of two genetic models for the innate components of colony odor in social Hymenoptera. Behav. Ecol. Sociobiol. 4, 217–224 (1979).Article 

Google Scholar 
86.Ozaki, M. et al. Behavior: ant nestmate and non-nestmate discrimination by a chemosensory sensillum. Science 309, 311–314 (2005).CAS 
PubMed 
Article 
ADS 

Google Scholar 
87.Starks, P. T. Recognition systems: from components to conservation. Ann. Zool. Fennici. 41, 689–690 (2004).
Google Scholar 
88.Franks, N., Blum, M., Smith, R. K. & Allies, A. B. Behavior and chemical disguise of cuckoo ant Leptothorax kutteri in relation to its host Leptothorax acervorum. J. Chem. Ecol. 16, 1431–1444 (1990).CAS 
PubMed 
Article 

Google Scholar 
89.Hernández, J. V. et al. Leaf-cutter ant species (Hymenoptera: Atta) differ in the types of cues used to differentiate between self and others. Anim. Behav. 71, 945–952 (2006).Article 

Google Scholar 
90.Nehring, V. et al. Chemical disguise of myrmecophilous cockroaches and its implications for understanding nestmate recognition mechanisms in leaf-cutting ants. BMC Ecol. 16, 1–11 (2016).Article 
CAS 

Google Scholar 
91.Hernández, J. V., López, H. & Jaffe, K. Nestmate recognition signals of the leaf-cutting ant Atta laevigata. J. Insect. Physiol. 48, 287–295 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
92.Howard, R. W. & Blomquist, G. J. Chemical ecology and biochemistry of insect hydrocarbons. Ann. Rev. Entomol. 27, 149–172 (1982).CAS 
Article 

Google Scholar 
93.Sturgis, S. J., Greene, M. J. & Gordon, D. M. Hydrocarbons on harvester ant (Pogonomyrmex barbatus) Middens Guide Foragers to the Nest. J. Chem. Ecol. 37, 514–524 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Greene, M. J. & Gordon, D. M. Cuticular hydrocarbons inform task decisions. Nature 423, 32–32 (2003).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
95.Sano, K., Bannon, N. & Greene, M. J. Pavement ant workers (Tetramorium caespitum) assess cues coded in cuticular hydrocarbons to recognize conspecific and heterospecific non-nestmate ants. J. Insect. Behav. 31, 186–199 (2018).Article 

Google Scholar 
96.Guillem, R. M., Drijfhout, F. P. & Martin, S. J. Species-specific cuticular hydrocarbon stability within European Myrmica Ants. J. Chem. Ecol. 42, 1052–1062 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
97.Sprenger, P. P. & Menzel, F. Cuticular hydrocarbons in ants (Hymenoptera: Formicidae) and other insects: how and why they differ among individuals, colonies, and species. Myrmec. News 30, 1–26 (2020).
98.Dahbi, A., Cerdá, X., Hefetz, A. & Lenoir, A. Social closure, aggressive behavior, and cuticular hydrocarbon profiles in the polydomous ant Cataglyphis iberica (Hymenoptera, Formicidae). J. Chem. Ecol. 22, 2173–2186 (1996).CAS 
PubMed 
Article 

Google Scholar 
99.Boulay, R., Katzav-Gozansky, T., Hefetz, A. & Lenoir, A. Odour convergence and tolerance between nestmates through trophallaxis and grooming in the ant Camponotus fellah (Dalla Torre). Insectes Soc. 51, 55–61 (2004).Article 

Google Scholar 
100.Dunn, R. R. & Messier, S. H. Evidence for the opposite of the dear enemy phenomenon in termites. J. Insect. Behav. 12, 461–464 (1999).Article 

Google Scholar 
101.Temeles, E. J., Muir, A. B., Slutsky, E. B. & Vitousek, M. N. Effect of food reductions on territorial behavior of purple-throated caribs. Condor 106, 691 (2004).Article 

Google Scholar 
102.Pacheco, P. S. M. & Del-Claro, K. Pseudomyrmex concolor Smith (Formicidae: Pseudomyrmecinae) as induced biotic defence for host plant Tachigali myrmecophila Ducke (Fabaceae: Caesalpinioideae). Ecol. Entomol. 43, 782–793 (2018).Article 

Google Scholar 
103.Hager, F. A. & Krausa, K. Acacia ants respond to plant-borne vibrations caused by mammalian browsers. Curr. Biol. 29, 717-725.e3 (2019).CAS 
PubMed 
Article 

Google Scholar  More